I feel like in the near future, every developer will have their own local LLM sitting right alongside their environment—just like how we all have VS Code, Visual Studio, or SQL Server Management Studio today. As data architects and developers, we’re often tempted to throw the biggest, most powerful API at every text-processing problem we encounter. Need a resume parsed? Call Claude. Need a user query categorized? Hit GPT-4. But when you’re processing thousands of documents, building high-volume automation pipelines, or handling proprietary application logs, relying entirely on external APIs introduces three major headaches: Spiraling token costsNetwork latency spikesData privacy… Read more
Tag: Enterprise AI
Most AI Products Don’t Fail at the Idea Stage
Lately, I’ve been hearing this a lot in corporate conversations: “We built an AI POC.”“We automated this with AI.”“Our AI demo was successful.” And honestly, many of them are successful at the POC stage. The models work.The demos look impressive.The presentations create excitement. But very few actually make it all the way to production and become part of real business operations. I think that’s one of the biggest gaps in the AI space right now. Not building the demo.Not proving the concept.But actually shipping something reliable, scalable, and usable in the real world. The demo environment is controlled.Production is not…. Read more